Hashtag-Based Learning Profile Enrichment for Personalized Recommendation in e-Learning Environments

Mérième Ghenname; Mounia Abik; Julien Subercaze; Christophe Gravier; Frédérique Laforest; Rachida Ajhoun

doi:10.15866/irecos.v10i9.6261

Hashtag-Based Learning Profile Enrichment for Personalized Recommendation in e-Learning Environments

Mérième Ghenname^(1*), Mounia Abik⁽²⁾, Julien Subercaze⁽³⁾, Christophe Gravier⁽⁴⁾, Frédérique Laforest⁽⁵⁾, Rachida Ajhoun⁽⁶⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v10i9.6261

Abstract

Recommendation within e-learning environments is not a new issue, it is even in the heart of reflection of researchers in this topic. In this paper, we outline our architecture for multidimensional recommendation in the e-learning environments, focused on personalized recommendation. For this purpose, we provide methodology for retrieving and analyzing hashtags contained in users’ writings on social networks called a personomy, to enrich automatically their profiles. We have defined a method to identify semantics of hashtags and semantic relationships between the meanings of different hashtags using the data mining and semantic Web technologies. By the way, we have defined the concept of Folksionary, as a hashtags dictionary that for each hashtag clusters its definitions into meanings. Semantized hashtags are thus used to feed the learner’s profile so as to make personalized recommendation on learning systems.
Copyright © 2015 Praise Worthy Prize - All rights reserved.

Keywords

Hashtags; Social Network; Semantic Web; e-Learning; Learning Profile; Personalized Recommendation

Full Text:

PDF

References

Lara, Ruben, Olmedilla, Daniel, Arroyo, Sinuhe, et al. A semantic web services framework for distributed e-learning environments. Informe técnico, L3S Research Center, 2004.

Chughtai, Muhammad Waseem, Selamat, Ali, Ghani, Imran, et al. E-learning recommender systems based on goal-based hybrid filtering. International Journal of Distributed Sensor Networks, 2014, vol. 2014.
http://dx.doi.org/10.1155/2014/912130

Zapata, Alfredo, Menéndez, Víctor H., Prieto, Manuel E., et al. Evaluation and selection of group recommendation strategies for collaborative searching of learning objects. International Journal of Human-Computer Studies, 2015, vol. 76, p. 22-39.
http://dx.doi.org/10.1016/j.ijhcs.2014.12.002

Chen, Chih-Ming, Lee, Hahn-Ming, et Chen, Ya-Hui. Personalized e-learning system using item response theory. Computers & Education, 2005, vol. 44, no 3, p. 237-255.
http://dx.doi.org/10.1016/j.compedu.2004.01.006

Klašnja-Milićević, Aleksandra, Vesin, Boban, Ivanović, Mirjana, et al. E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 2011, vol. 56, no 3, p. 885-899.
http://dx.doi.org/10.1016/j.compedu.2010.11.001

Florence Sèdes, Mihaela Brut. Modélisation basée sur des ontologies pour développer des recommandations personnalisées dans les systèmes hypermédia adaptatifs, Dans : INFormatique des Organisations et Systemes d'Information et de Decision (INFORSID 2010), Marseille, 25/05/2010-28/05/2010, Régine Laleau (Eds.), Association INFORSID, p. 61-76, mai 2010.

Berkani, Lamia, Chikh, Azeddine, et Nouali, Omar. Recommandation personnalisée des ressources dans une communauté de pratique de e-learning. Une approche à base de filtrage hybride. INFORSID 2013. p. 131-138.

Zouag, Rafika. Filtrage collaboratif des objets pédagogiques. 2014. Thèse de doctorat.

Khribi, Mohamed Koutheaïr, Jemni, Mohamed, et Nasraoui, Olfa. Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. In : Advanced Learning Technologies, 2008. ICALT'08. Eighth IEEE International Conference on. IEEE, 2008. p. 241-245.
http://dx.doi.org/10.1109/icalt.2008.198

IMS Global Learning Consortium, Inc, et al. IMS learning design information model. IMS-LD), version, 2003, vol. 1.

LESK, Michael. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In : Proceedings of the 5th annual international conference on Systems documentation. ACM, 1986. p. 24-26.
http://dx.doi.org/10.1145/318723.318728

Gale, William A., Church, Kenneth W., et Yarowsky, David. A method for disambiguating word senses in a large corpus. Computers and the Humanities, 1992, vol. 26, no 5-6, p. 415-439.
http://dx.doi.org/10.1007/bf00136984

Yarowsky, David. Unsupervised word sense disambiguation rivaling supervised methods. In : Proceedings of the 33rd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 1995. p. 189-196.
http://dx.doi.org/10.3115/981658.981684

Ide, Nancy et Véronis, Jean. Introduction to the special issue on word sense disambiguation: the state of the art. Computational linguistics, 1998, vol. 24, no 1, p. 2-40.
http://dx.doi.org/10.3115/997939.998006

Small, Steven Lawrence. Word expert parsing: A theory of distributed word-based natural language understanding, Doctoral Dissertation, University of Maryland at College Park College Park, MD, USA ©1980.

Dahlgren, Kathleen. Naive semantics for natural language understanding. Dordrecht : Kluwer, 1988.
http://dx.doi.org/10.1007/978-1-4613-1075-4

Lesk, Michael. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In : Proceedings of the 5th annual international conference on Systems documentation. ACM, 1986. p. 24-26.
http://dx.doi.org/10.1145/318723.318728

Ghenname, Merieme, Subercaze, Julien, Gravier, Christophe, et al. A hashtags dictionary from crowdsourced definitions. In : Data Engineering Workshops (ICDEW), 2014 IEEE 30th International Conference on. IEEE, 2014. p. 39-44.
http://dx.doi.org/10.1109/icdew.2014.6818300

Banerjee, Satanjeev et Pedersen, Ted. An adapted Lesk algorithm for word sense disambiguation using WordNet. In : Computational linguistics and intelligent text processing. Springer Berlin Heidelberg, 2002. p. 136-145.
http://dx.doi.org/10.1007/3-540-45715-1_11

Enright, Anton J., Van Dongen, Stijn, et Ouzounis, Christos A. An efficient algorithm for large-scale detection of protein families. Nucleic acids research, 2002, vol. 30, no 7, p. 1575-1584.
http://dx.doi.org/10.1093/nar/30.7.1575

Brohee, Sylvain et Van Helden, Jacques. Evaluation of clustering algorithms for protein-protein interaction networks. BMC bioinformatics, 2006, vol. 7, no 1, p. 488.
http://dx.doi.org/10.1186/1471-2105-7-488

Bruynooghe, M. Classification ascendante hiérarchique des grands ensembles de données: un algorithme rapide fondé sur la construction des voisinages réductibles. Cahiers de l'Analyse des Données, 1978, vol. 3, no 1, p. 7-33.

Oubahssi, Lahcen. Conception de plates-formes logicielles pour la formation à distance, présentant des propriétés d'adaptabilité à différentes catégories d'usagers et d'interopérabilité avec d'autres environnements logiciels. 2005. Thèse de doctorat. Paris 5.

Refbacks

There are currently no refbacks.

Username
Password
Remember me